Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR)

In the recent economic crises, one of the precise uniqueness that all stock markets have in common is the uncertainty. An attempt was made to forecast future index of the Malaysia Stock Exchange Market using artificial neural network (ANN) model and a traditional forecasting tool – Multiple Linear Regressions (MLR). This paper starts with a brief introduction of stock exchange of Malaysia, an overview of artificial neural network and machine learning models used for prediction. System design and data normalization using MINITAB software were described. Training algorithm, MLR Model and network parameter models were presented. Best training graphs showing the training, validation, test and all regression values were analyzed.

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Solving multiple linear regression problem using artificial neural network

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i1.pp770-775 ◽

2022 ◽

Vol 12 (1) ◽

pp. 770

Author(s):

Mohammad S. Khrisat ◽

Ziad A. Alqadi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Problem ◽

Simple Method ◽

Scientific Experiments ◽

Linear Regression Problem ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The Relationship ◽

Linear Regressions

<span>Multiple linear regressions are an important tool used to find the relationship between a set of variables used in various scientific experiments. In this article we are going to introduce a simple method of solving a multiple rectilinear regressions (MLR) problem that uses an artificial neural network to find the accurate and expected output from MLR problem. Different artificial neural network (ANN) types with different architecture will be tested, the error between the target outputs and the calculated ANN outputs will be investigated. A recommendation of using a certain type of ANN based on the experimental results will be raised.</span>

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Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions

Data in Brief ◽

10.1016/j.dib.2018.08.205 ◽

2018 ◽

Vol 20 ◽

pp. 1462-1467 ◽

Cited By ~ 1

Author(s):

Majid Radfard ◽

Hamed Soleimani ◽

Samira Nabavi ◽

Bayram Hashemzadeh ◽

Hesam Akbari ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Sodium Absorption ◽

Multiple Linear Regressions ◽

Sodium Absorption Ratio ◽

Absorption Ratio ◽

Artificial Neural ◽

Linear Regressions

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Usporedna studija predviđanja koeficijenta molekularne difuzije za polarni i nepolarni binarni plin pomoću neuronskih mreža i višestrukih linearnih regresija

Kemija u industriji ◽

10.15255/kui.2019.010 ◽

2019 ◽

Vol 68 (11-12) ◽

pp. 573-582 ◽

Cited By ~ 1

Author(s):

Naima Melzi ◽

Hamid Zentou ◽

Maamar Laidi ◽

Salah Hanini ◽

Yamina Ammi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Molecular Diffusion ◽

Mean Squared Error ◽

External Validation ◽

Multiple Linear Regressions ◽

Mean Squared Errors ◽

Wide Range ◽

Artificial Neural ◽

Linear Regressions

In the current study, an artificial neural network (ANN) and multiple linear regressions (MLR) have been used to develop predictive models for the estimation of molecular diffusion coefficients of 1252 polar and non-polar binary gases at multiple pressures over a wide range of temperatures and substances. The quality and reliability of each method were estimated in terms of the correlation coefficient (R), mean squared errors (MSE), root mean squared error (RMSE), and in terms of external validation coefficients (Q2ext). The comparison between the artificial neural network (ANN) and the multiple linear regressions (MLR) revealed that the neural network models showed a good predicting ability with lower errors (the roots of the mean squared errors in the total database were 0.1400 for ANN1 and 0.1300 for ANN2), and (root mean squared errors in the total databases were 0.5172 for MLR1 and 0.5000 for MLR2).

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Prediction of uniaxial compressive strength of carbonate rocks and cement mortar using artificial neural network and multiple linear regressions

Acta Geodynamica et Geomaterialia ◽

10.13168/agg.2020.0027 ◽

2020 ◽

pp. 367-377

Author(s):

Mohamed Abdelhedi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Compressive Strength ◽

Uniaxial Compressive Strength ◽

Carbonate Rocks ◽

Cement Mortar ◽

Multiple Linear Regressions ◽

Artificial Neural ◽

Linear Regressions

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Prediction Tourist Visits With Multiple Linear Regressions in Artificial Neural Networks

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i3.949 ◽

2021 ◽

Vol 12 (3) ◽

pp. 1492-1501

Author(s):

Rini Sovia Et.al

Keyword(s):

Neural Network ◽

Prediction Models ◽

Predictor Variable ◽

Predictor Variables ◽

Multiple Linear Regressions ◽

Test Results ◽

Tourism Sector ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Linear Regressions

Tourist visit is a topic of discussion that has been much researched by previous researchers in conducting a prediction process. Many prediction models have been produced that refers to the use of several methods to obtain output in the form of information that is needed by the tour manager. Judging from the results of the study, it is still only focused on the discussion in producing output without testing the correlation of variables used as predictors. The problem in this study is how to predict the number of tourist visits by using Multiple Linear Regression (MLR) as a correlation test predictor variable and Artificial Neural Network (ANN) as a calculating machine in making predictions. The implementation of these two methods is very suitable to be used in terms of prediction, where the MLR method test results show the correlation of predictor variables used namely xxx correlation. Then in the prediction process that has been done to produce output with an accuracy value of xx%, the value of MSE xx% and RMSE is xx. Therefore, this research will be useful for managers of the tourism sector so that the goal achieved from this research is to assist the tourism office in seeing how many visits will occur in the next period.

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